NRITYAM: Language Models Meet Art and Heritage of Dance

• URL: http://arxiv.org/abs/2606.19727v1
• Date: Thu, 18 Jun 2026 02:50:40 GMT
• Title: NRITYAM: Language Models Meet Art and Heritage of Dance
• Abstract summary: NRITYAM comprises 9,260 carefully curated question-answer pairs spanning 12 languages.<n>The dataset has been developed from the ground up through close collaboration with native dance artists and native speakers of the languages.<n>As a multilingual and multicultural benchmark, NRITYAM sets a new standard for evaluating the ability of AI systems to understand and reason about traditional performing arts.
• Score: 9.272285505965455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Language models have become essential tools in shaping modern workflows. However, their global effectiveness hinges on a nuanced understanding of local socio-cultural contexts. To address this gap, we present NRITYAM, a comprehensive benchmark for evaluating the cultural comprehension capabilities of language models in the context of global dance traditions. NRITYAM comprises 9,260 carefully curated question-answer pairs spanning 12 languages, making it the largest dataset dedicated to evaluating cultural knowledge in dance. The dataset has been developed from the ground up through close collaboration with native dance artists and native speakers of the languages, who authored and validated culturally relevant questions specific to their regions. We evaluate a broad set of models, including large language models, small language models, multimodal large language models, and small multimodal language models. As a multilingual and multicultural benchmark, NRITYAM sets a new standard for evaluating the ability of AI systems to understand and reason about traditional performing arts. Detailed dataset samples are available at~\url{https://github.com/niladrighosh03/NRITYAM}.

Related papers

• Do You Know About My Nation? Investigating Multilingual Language Models' Cultural Literacy Through Factual Knowledge [68.6805229085352]
  Most multilingual question-answering benchmarks do not factor in regional diversity in the information they capture.<n>XNationQA encompasses a total of 49,280 questions on the geography, culture, and history of nine countries, presented in seven languages.<n>We benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics.
  arXiv  Detail & Related papers  (2025-11-01T18:41:34Z)
• From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledge [7.322034156204158]
  We show that large language models (LLMs) struggle with cultural knowledge and performance when context is provided.<n>Our work addresses these limitations through a Bengali Language Cultural Knowledge dataset including folk traditions, culinary arts, and regional dialects.<n>Our investigation of several multilingual language models shows that while these models perform well in non-cultural categories, they struggle significantly with cultural knowledge and performance improves substantially when context is provided.
  arXiv  Detail & Related papers  (2025-10-22T21:42:59Z)
• DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture [14.681676046750342]
  DRISHTIKON is a first-of-its-kind multimodal and multilingual benchmark centered exclusively on Indian culture.<n>The dataset captures rich cultural themes including festivals, attire, cuisines, art forms, and historical heritage.<n>We evaluate a wide range of vision-language models (VLMs), including open-source small and large models, proprietary systems, reasoning-specialized VLMs, and Indic-focused models.
  arXiv  Detail & Related papers  (2025-09-23T17:40:43Z)
• Grounding Multilingual Multimodal LLMs With Cultural Knowledge [48.95126394270723]
  We propose a data-centric approach that grounds MLLMs in cultural knowledge.<n>CulturalGround comprises 22 million high-quality, culturally-rich VQA pairs spanning 42 countries and 39 languages.<n>We train an open-source MLLM CulturalPangea on CulturalGround, interleaving standard multilingual instruction-tuning data to preserve general abilities.
  arXiv  Detail & Related papers  (2025-08-10T16:24:11Z)
• SANSKRITI: A Comprehensive Benchmark for Evaluating Language Models' Knowledge of Indian Culture [17.414561140897945]
  We introduce SANSKRITI, a benchmark designed to evaluate language models' comprehension of India's rich cultural diversity.<n>Comprising 21,853 meticulously curated question-answer pairs spanning 28 states and 8 union territories, SANSKRITI is the largest dataset for testing Indian cultural knowledge.<n>It covers sixteen key attributes of Indian culture: rituals and ceremonies, history, tourism, cuisine, dance and music, costume, language, art, festivals, religion, medicine, transport, sports, nightlife, and personalities.
  arXiv  Detail & Related papers  (2025-06-18T11:19:25Z)
• MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs [37.98920430188422]
  MAKIEval is an automatic multilingual framework for evaluating cultural awareness in large language models.<n>It automatically identifies cultural entities in model outputs and links them to structured knowledge.<n>We assess 7 LLMs developed from different parts of the world, encompassing both open-source and proprietary systems.
  arXiv  Detail & Related papers  (2025-05-27T19:29:40Z)
• CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark [68.21939124278065]
  Culturally-diverse multilingual Visual Question Answering benchmark designed to cover a rich set of languages and cultures. CVQA includes culturally-driven images and questions from across 30 countries on four continents, covering 31 languages with 13 scripts, providing a total of 10k questions. We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and show that the dataset is challenging for the current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-10T01:59:00Z)
• Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
  This paper introduces a novel approach for massively multicultural knowledge acquisition. Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages. Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
  arXiv  Detail & Related papers  (2024-02-14T18:16:54Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction [102.13536517783837]
  Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. We discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.
  arXiv  Detail & Related papers  (2023-06-11T23:27:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.